ArticleWorld journal of gastrointestinal oncology2024
Development and validation of a nomogram for predicting lymph node metastasis in early gastric cancer.
Article in World journal of gastrointestinal oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Endoscopic resection or radical gastrectomy? Risk factors for lymph node metastasis in early gastric cancer: a systematic review and meta-analysis.World journal of surgical oncology · 2026Pooled it
- Artificial Intelligence in Gastric Cancer: Diagnostic, Prognostic, and Predictive Developments, Evidence Maturity, and Translational Challenges.Cancer reports (Hoboken, N.J.) · 2026Review
- Real-World Evidence for Expanding the Criteria for Endoscopic Resection in Submucosal Gastric Cancer: The Role of Invasion Width.Journal of gastric cancer · 2026Article
- Lactylation-drived TRIM29 induces invasive behavior and lymph node metastasis in gastric cancer via hnRNPA1-mediated Wnt/β-catenin pathway.Cell death & disease · 2026Article
- Article
- Combining biomarkers to construct a novel predictive model for predicting preoperative lymph node metastasis in early gastric cancer.Frontiers in oncology · 2025Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLymph node metastasis (LNM) significantly impacts the treatment and prognosis of early gastric cancer (EGC). Consequently, the precise prediction of LNM risk in EGC patients is essential to guide the selection of appropriate surgical approaches in clinical settings.
aimTo develop a novel nomogram risk model for predicting LNM in EGC patients, utilizing preoperative clinicopathological data.
methodsUnivariate and multivariate logistic regression analyses were performed to examine the correlation between clinicopathological factors and LNM in EGC patients. Additionally, univariate Kaplan-Meier and multivariate Cox regression analyses were used to assess the influence of clinical factors on EGC prognosis. A predictive model in the form of a nomogram was developed, and its discrimination ability and calibration were also assessed.
resultsThe incidence of LNM in the study cohort was 19.6%. Multivariate logistic regression identified tumor size, location, degree of differentiation, and pathological type as independent risk factors for LNM in EGC patients. Both tumor pathological type and LNM independently affected the prognosis of EGC. The model's performance was reflected by an area under the curve of 0.750 [95% confidence interval (CI): 0.701-0.789] for the training group and 0.763 (95%CI: 0.687-0.838) for the validation group.
conclusionA clinical prediction model was constructed (using tumor size, low differentiation, location in the middle-lower region, and signet ring cell carcinoma), with its score being a significant prognosis indicator.
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